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All Journal Journal of Computers and Digital Business
Amira Hassan Abed
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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The Contribution of Artificial Intelligence to Addressing the Global Goals for Sustainable Development Hany Fathy Abdel-Elaah; Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.633

Abstract

The increasing prevalence of Artificial Intelligence (AI) across various industries necessitates an assessment of its impact on achieving the Sustainable Development Goals (SDGs). Studies indicate that AI has the potential to support 134 targets across all goals through professional, consensus-based data collection strategies. However, it may also hinder progress toward 59 targets, presenting a complex interplay between benefits and challenges. Key concerns include gaps in safety, transparency, and ethical standards, which arise when regulatory frameworks fail to keep pace with the rapid advancement of AI technologies. These issues highlight the need for robust governance and oversight mechanisms to address potential risks. Additionally, overlooked components in the study, such as social equity, environmental justice, and accessibility, are critical for ensuring AI-based solutions contribute effectively to sustainable growth. This research emphasizes the importance of aligning AI applications with global regulatory and ethical standards to maximize positive outcomes while mitigating adverse effects. By fostering collaboration among policymakers, industry leaders, and researchers, AI can become a transformative tool for achieving SDGs. Future efforts should prioritize addressing regulatory gaps and ensuring that AI-driven innovation remains inclusive, transparent, and aligned with the core principles of sustainability.
Artificial Intelligence-Driven Pharmaceutical Research: A Comprehensive Analysis of Applications and Challenges Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 1 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i1.634

Abstract

This review investigates the integration of Artificial Intelligence (AI) in pharmaceutical product development, focusing on its applications in drug discovery, design, manufacturing, and quality control. Key AI methodologies, such as machine learning (ML) and deep learning (DL), are analyzed for their contributions to critical stages, including target identification, molecular screening, and clinical trial optimization. The findings highlight AI's capacity to streamline workflows, reduce development costs, and enhance efficacy, with notable improvements in drug discovery speed, prediction accuracy of drug safety and efficacy, and novel approaches in drug repurposing and personalized medicine. Despite these advancements, challenges such as fragmented data integration, limited availability of specialized skillsets, and resistance to AI adoption remain significant barriers. This review emphasizes the need for industry-wide collaboration to address these issues and leverage AI's full potential. In conclusion, AI demonstrates transformative capabilities in accelerating drug development cycles and enabling precision-driven innovations, promising a paradigm shift in pharmaceutical practices through the convergence of computational power and biological sciences.
Machine Learning (ML) Algorithms for Diagnosing Blood Cancer in Blood Smear Images Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 2 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i2.756

Abstract

Artificial intelligence (AI), particularly deep learning (DL), has significantly advanced medical image analysis, including the detection and classification of blood cancer through blood smear images. This review explores the state-of-the-art data mining (DM) and DL techniques applied in the identification and classification of white blood cells (WBCs), with a focus on leukemia diagnosis. By systematically analyzing relevant literature from 2014 to 2024, the study highlights key AI algorithms, including traditional machine learning models such as SVM, KNN, and ANN, as well as modern DL architectures like CNN, RCNN, ResNet, and hybrid models. The review evaluates their performance, clinical applicability, and implementation challenges. Particular attention is given to the strengths of DL in feature extraction and classification accuracy, which often surpass traditional DM approaches. Despite these advances, issues such as data scarcity, computational cost, and the need for medical expertise remain major challenges. The study also outlines future directions involving lightweight DL models, transfer learning, and open-access datasets to enhance clinical deployment. Ultimately, this work provides a comprehensive foundation for researchers and developers aiming to improve blood cancer diagnosis through automated medical imaging systems powered by AI.
The Hybrid Deep Learning ANN-CNN Model for Enhancing Diabetes Prediction Amira Hassan Abed
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.766

Abstract

Diabetes mellitus is a global chronic metabolic disease that poses a serious threat to human health. Accurate and early prediction of diabetes is essential for effective medical treatment and long-term disease management. In this study, we propose a deep learning–based framework as a novel approach for diabetes prediction using a large-scale dataset containing more than 6,000 patient records. Several deep learning architectures, including Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), are examined to determine the most effective model for prediction tasks. Building on the strengths of both methods, this research introduces a hybrid ANN–CNN architecture designed to leverage ANN’s capability in learning nonlinear relationships and CNN’s efficiency in extracting high-level feature patterns. Extensive data preprocessing and feature extraction were conducted to enhance model performance and ensure reliable outcomes. Experimental results demonstrate that the hybrid ANN–CNN model achieved the highest prediction accuracy of 91.4%, surpassing standalone ANN (86.2%) and CNN (88.9%) models. These findings highlight the potential of hybrid deep learning frameworks in improving clinical decision support systems, enabling more accurate risk assessment and early intervention for diabetes. The results further indicate that integrating complementary neural network structures can significantly enhance predictive performance in complex medical datasets.
Co-Authors Hany Fathy Abdel-Elaah